Pub Date : 2020-01-01DOI: 10.1504/ijw.2020.10029814
Marjan Ghanbarian, M. Ghanbarian, Aliakbar Roudbari, A. Salehi, A. Javid
{"title":"The effect of conventional water disinfection methods on nitrate level.","authors":"Marjan Ghanbarian, M. Ghanbarian, Aliakbar Roudbari, A. Salehi, A. Javid","doi":"10.1504/ijw.2020.10029814","DOIUrl":"https://doi.org/10.1504/ijw.2020.10029814","url":null,"abstract":"","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":"1 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66703036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Javid, Aliakbar Roudbari, A. Salehi, M. Ghanbarian, Marjan Ghanbarian
{"title":"The effect of conventional water disinfection methods on nitrate level","authors":"A. Javid, Aliakbar Roudbari, A. Salehi, M. Ghanbarian, Marjan Ghanbarian","doi":"10.1504/ijw.2020.112710","DOIUrl":"https://doi.org/10.1504/ijw.2020.112710","url":null,"abstract":"","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66703100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/IJW.2020.10035275
I. Hossain, H. Rasel, F. Mekanik, M. Imteaz
This paper presents the efficiency of non-linear modelling technique in predicting long-term seasonal rainfall of Western Australia. One of the commonly used non-linear modelling approaches, artificial neural network (ANN) was adopted for the construction of the non-linear models. The models were developed considering the past values of El Nino southern oscillation (ENSO) and Indian Ocean Dipole (IOD) as the probable influential variables of rainfall. The ANN models were constructed adopting the algorithm proposed by Lavenberg-Marquardt. The models were developed and tested for three rainfall stations in Western Australia. The models showed good generalisation capability of Western Australian spring rainfalls with Pearson correlations varying from 0.46 to 0.82 during the training phase and 0.55 to 0.96 during the testing phase. The errors and index of agreement of the IOD-ENSO based ANN models were also acceptable to be applied for rainfall forecasting.
{"title":"Artificial neural network modelling technique in predicting Western Australian seasonal rainfall","authors":"I. Hossain, H. Rasel, F. Mekanik, M. Imteaz","doi":"10.1504/IJW.2020.10035275","DOIUrl":"https://doi.org/10.1504/IJW.2020.10035275","url":null,"abstract":"This paper presents the efficiency of non-linear modelling technique in predicting long-term seasonal rainfall of Western Australia. One of the commonly used non-linear modelling approaches, artificial neural network (ANN) was adopted for the construction of the non-linear models. The models were developed considering the past values of El Nino southern oscillation (ENSO) and Indian Ocean Dipole (IOD) as the probable influential variables of rainfall. The ANN models were constructed adopting the algorithm proposed by Lavenberg-Marquardt. The models were developed and tested for three rainfall stations in Western Australia. The models showed good generalisation capability of Western Australian spring rainfalls with Pearson correlations varying from 0.46 to 0.82 during the training phase and 0.55 to 0.96 during the testing phase. The errors and index of agreement of the IOD-ENSO based ANN models were also acceptable to be applied for rainfall forecasting.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66703049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/IJW.2020.10035276
David Rossi, D. Dobrzyński, I. Moro, M. D. Zotto, E. Moschin, N. Realdon
Wettability research was orchestrated with chemical and biological (microalgae, bacteria) characteristics in multidisciplinary assessment of different types of waters (groundwater, surface water, disinfected drinking water) from the area of Lourdes town (Haute Pyrenees Department, France). Multidisciplinary data were subjected to statistical analysis. The molecular analysis indicated that isolated cultivable bacterial strains belonging to γ-Proteobacteria significantly dominate over bacteria classified within the Flavobacteriia and β-Proteobacteria groups. The correlations between the contact angles and the chemical parameters suggest their influences on the surface tension characteristics of the waters. The relations between the contact angle and microbiological data confirm the influence of environmental conditions on the biological and surface tension parameters of the waters. Implementation of surface tensiometry technique to the study of the biological and geochemical status of groundwater has not previously been performed. This work encourages applications that aim at the assessment and understanding of groundwater ecosystems.
{"title":"Contact angle keying method for an integrated analytical approach to characterising waters in the Lourdes area (Pyrenees, France)","authors":"David Rossi, D. Dobrzyński, I. Moro, M. D. Zotto, E. Moschin, N. Realdon","doi":"10.1504/IJW.2020.10035276","DOIUrl":"https://doi.org/10.1504/IJW.2020.10035276","url":null,"abstract":"Wettability research was orchestrated with chemical and biological (microalgae, bacteria) characteristics in multidisciplinary assessment of different types of waters (groundwater, surface water, disinfected drinking water) from the area of Lourdes town (Haute Pyrenees Department, France). Multidisciplinary data were subjected to statistical analysis. The molecular analysis indicated that isolated cultivable bacterial strains belonging to γ-Proteobacteria significantly dominate over bacteria classified within the Flavobacteriia and β-Proteobacteria groups. The correlations between the contact angles and the chemical parameters suggest their influences on the surface tension characteristics of the waters. The relations between the contact angle and microbiological data confirm the influence of environmental conditions on the biological and surface tension parameters of the waters. Implementation of surface tensiometry technique to the study of the biological and geochemical status of groundwater has not previously been performed. This work encourages applications that aim at the assessment and understanding of groundwater ecosystems.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66703055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1504/IJW.2020.10035284
Shubra Jain, Ankit Kumar Parida, S. Sankaranarayanan
Water is a big challenge not only in India but in many countries of the world. Machine learning and forecasting model has been employed towards water demand and ground water level prediction. But in terms of water scarcity, much less work has been carried out by employing machine learning algorithms like 'artificial neural network' (ANN) and 'grey forecasting' model for forecasting water scarcity and none has focused on historical data like water availability, water consumption for a particular area and stress value for predicting water scarcity. So accordingly, we here have developed a water scarcity prediction system based on historical data by employing 'deep neural networks' which is an advanced form of 'artificial neural networks'. We have also compared 'deep neural network' with existing machine learning algorithms such as "support vector machine (SVM), logistic regression and Naive Bayes". From the analysis of algorithms based on dataset, deep neural networks have been found as the best prediction model for water scarcity.
{"title":"Water scarcity prediction for global region using machine learning","authors":"Shubra Jain, Ankit Kumar Parida, S. Sankaranarayanan","doi":"10.1504/IJW.2020.10035284","DOIUrl":"https://doi.org/10.1504/IJW.2020.10035284","url":null,"abstract":"Water is a big challenge not only in India but in many countries of the world. Machine learning and forecasting model has been employed towards water demand and ground water level prediction. But in terms of water scarcity, much less work has been carried out by employing machine learning algorithms like 'artificial neural network' (ANN) and 'grey forecasting' model for forecasting water scarcity and none has focused on historical data like water availability, water consumption for a particular area and stress value for predicting water scarcity. So accordingly, we here have developed a water scarcity prediction system based on historical data by employing 'deep neural networks' which is an advanced form of 'artificial neural networks'. We have also compared 'deep neural network' with existing machine learning algorithms such as \"support vector machine (SVM), logistic regression and Naive Bayes\". From the analysis of algorithms based on dataset, deep neural networks have been found as the best prediction model for water scarcity.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66703065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-30DOI: 10.1504/IJW.2019.10022807
F. Islam, M. Imteaz
The aim of the study was to develop a model to forecast autumn rainfall several months in advance for south-west division (SWD) of Western Australia (WA), by identifying and incorporating the relationship among major climate indices such as dipole mode index (DMI), southern oscillation index (SOI), ENSO Modoki index (EMI) and autumn rainfall. Eight rainfall stations from two regions of SWD were considered. Statistical analysis showed that DMI, SOI, Nino3.4, Nino3 and Nino4 have significant correlations with autumn rainfall for all these stations. On the other hand, EMI showed significant correlations for the stations in the north-coast region only. Meanwhile, DMI effect has been found stronger for all the stations compared to other climate indices. Several multiple regression analyses were conducted using lagged ENSO-DMI, lagged SOI-DMI and lagged EMI-DMI indices, and significant increase in the correlations between autumn rainfall and climate indices was observed. However, only statistically significant models were suggested.
{"title":"Development of prediction model for forecasting rainfall in Western Australia using lagged climate indices","authors":"F. Islam, M. Imteaz","doi":"10.1504/IJW.2019.10022807","DOIUrl":"https://doi.org/10.1504/IJW.2019.10022807","url":null,"abstract":"The aim of the study was to develop a model to forecast autumn rainfall several months in advance for south-west division (SWD) of Western Australia (WA), by identifying and incorporating the relationship among major climate indices such as dipole mode index (DMI), southern oscillation index (SOI), ENSO Modoki index (EMI) and autumn rainfall. Eight rainfall stations from two regions of SWD were considered. Statistical analysis showed that DMI, SOI, Nino3.4, Nino3 and Nino4 have significant correlations with autumn rainfall for all these stations. On the other hand, EMI showed significant correlations for the stations in the north-coast region only. Meanwhile, DMI effect has been found stronger for all the stations compared to other climate indices. Several multiple regression analyses were conducted using lagged ENSO-DMI, lagged SOI-DMI and lagged EMI-DMI indices, and significant increase in the correlations between autumn rainfall and climate indices was observed. However, only statistically significant models were suggested.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45642791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-30DOI: 10.1504/IJW.2019.10022798
A. Dehnavi, P. Goudarzian
Increasing measurements costs for surface water quality assessment besides the need for continuous measurements will cause a dilemma in poor countries. Therefore, a new method is proposed to identify and predict minimum probable water quality index (MIP-WQI) and maximum probable WQI (MAP-WQI) values instead of the traditional ones by combining available WQI data and Taguchi method. The water quality data of Bilghan station on Karaj River was used for prediction and comparison of the MIP-WQI and the MAP-WQI. According to the surveys and based on proposed method, the MIP-WQI and the MAP-WQI values based on 2008's data were estimated to be 61.6 and 87.4, respectively. Whereas from 2008 to 2010, actual minimum WQI values were 65.9, 69.8 and 69.3, respectively. In addition, actual maximum WQI values were 83.1, 77.2 and 75.6, respectively. Moreover, these probable indices could be more suitable to be used for water management especially in poor and underdeveloped countries.
{"title":"Proposing the minimum and maximum probable water quality indices for better water quality management in poor and underdeveloped countries (case study: Bilghan intake)","authors":"A. Dehnavi, P. Goudarzian","doi":"10.1504/IJW.2019.10022798","DOIUrl":"https://doi.org/10.1504/IJW.2019.10022798","url":null,"abstract":"Increasing measurements costs for surface water quality assessment besides the need for continuous measurements will cause a dilemma in poor countries. Therefore, a new method is proposed to identify and predict minimum probable water quality index (MIP-WQI) and maximum probable WQI (MAP-WQI) values instead of the traditional ones by combining available WQI data and Taguchi method. The water quality data of Bilghan station on Karaj River was used for prediction and comparison of the MIP-WQI and the MAP-WQI. According to the surveys and based on proposed method, the MIP-WQI and the MAP-WQI values based on 2008's data were estimated to be 61.6 and 87.4, respectively. Whereas from 2008 to 2010, actual minimum WQI values were 65.9, 69.8 and 69.3, respectively. In addition, actual maximum WQI values were 83.1, 77.2 and 75.6, respectively. Moreover, these probable indices could be more suitable to be used for water management especially in poor and underdeveloped countries.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48280450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-30DOI: 10.1504/IJW.2019.10022793
S. Armal, Rafea Al-Suhili
This study develops a modified cellular automata (CA) model to simulate the flash flood inundation extent on a case study of an urban sub-catchment, in New York City. Based on the soil composition, the Horton equation is modified with threshold infiltration rates and applied to different land cover types. Further, the orifice equation is updated with a time variant parameter to account for partial/full blockage in the inlets. We propose a slope weighted flow transfer function to adjust the CA model and address the problem of depth positivity and flow regime changes, occurring due to the partial submergence. Seven ponding points with different levels of inundation are detected in the survey of the area and accordingly compared with the output of the simulation. The results prove the applicability of the developed CA model to reproduce the evolution of water depth.
{"title":"An urban flood inundation model based on cellular automata","authors":"S. Armal, Rafea Al-Suhili","doi":"10.1504/IJW.2019.10022793","DOIUrl":"https://doi.org/10.1504/IJW.2019.10022793","url":null,"abstract":"This study develops a modified cellular automata (CA) model to simulate the flash flood inundation extent on a case study of an urban sub-catchment, in New York City. Based on the soil composition, the Horton equation is modified with threshold infiltration rates and applied to different land cover types. Further, the orifice equation is updated with a time variant parameter to account for partial/full blockage in the inlets. We propose a slope weighted flow transfer function to adjust the CA model and address the problem of depth positivity and flow regime changes, occurring due to the partial submergence. Seven ponding points with different levels of inundation are detected in the survey of the area and accordingly compared with the output of the simulation. The results prove the applicability of the developed CA model to reproduce the evolution of water depth.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41878063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-30DOI: 10.1504/IJW.2019.10022787
Arpan Pradhan, K. Khatua
Reliable prediction of discharge is the foremost requirement for the safety of river work and flood management. Parameters related to channel geometry and flow characteristics including effects of secondary current produced along the flow, momentum transfer across the main channel and floodplain are discussed briefly. In total seven datasets are used for the study. Discharge prediction for meandering channels by three existing methodologies, i.e., Greenhill and Sellin (1993), methods by James and Wark (1992) and by Shiono et al. (1999) is analysed. Relative error is calculated to check the degree of accuracy given by each method and is used as a tool to decide the effectiveness of the methods. The methods by James and Wark (1992) are observed to provide better results in comparison to the other models as they take into account the energy loss due to friction factor as well as sinuosity, geometry and expansion-contraction losses.
{"title":"Discharge prediction in meandering compound channels","authors":"Arpan Pradhan, K. Khatua","doi":"10.1504/IJW.2019.10022787","DOIUrl":"https://doi.org/10.1504/IJW.2019.10022787","url":null,"abstract":"Reliable prediction of discharge is the foremost requirement for the safety of river work and flood management. Parameters related to channel geometry and flow characteristics including effects of secondary current produced along the flow, momentum transfer across the main channel and floodplain are discussed briefly. In total seven datasets are used for the study. Discharge prediction for meandering channels by three existing methodologies, i.e., Greenhill and Sellin (1993), methods by James and Wark (1992) and by Shiono et al. (1999) is analysed. Relative error is calculated to check the degree of accuracy given by each method and is used as a tool to decide the effectiveness of the methods. The methods by James and Wark (1992) are observed to provide better results in comparison to the other models as they take into account the energy loss due to friction factor as well as sinuosity, geometry and expansion-contraction losses.","PeriodicalId":39788,"journal":{"name":"International Journal of Water","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45209382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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